docs: def preprocess(self, raw_inputs):
          """Convert images into the format required by our model.
  
          Our model requires that inputs be grayscale (mode 'L'), be resized to
          `MNIST_DIM`, and be represented as float32 numpy arrays in range
          [0, 1].
  
          Args:
              raw_inputs (list of Images): a list of PIL Image objects
  
          Returns:
              array (float32): num images * height * width * num channels
  
          """
          image_arrays = []
          for raw_im in raw_inputs:
              im = raw_im.convert('L')
              im = im.resize(MNIST_DIM, Image.ANTIALIAS)
              arr = np.array(im)
              image_arrays.append(arr)
  
          inputs = np.array(image_arrays)
          return inputs.reshape(len(inputs),
                                MNIST_DIM[0],
                                MNIST_DIM[1], 1).astype('float32') / 255